This project concerns the development of statistical techniques for the analysis of a variety of data sets relating to HIV disease, uterine fibroids and other diseases including STIs. With regard to HIV, we will focus primarily on causal inference techniques for the analysis of randomized trials for HIV prevention. Causal effects of new methods for prevention of HIV infection are complicated by the concurrent use of traditional prevention methods (eg. condoms) in both arms of a trial, and by non-compliance with the randomized intervention and with the other concurrent methods. Second, we will develop statistical methods for modeling time-to-event outcomes with complex features including current status outcomes, competing risks, multiple ordered events or states, measurement error, and partially known or imperfectly measured predictors. These tools will be developed for the purpose of analyzing HIV transmission risk within monogamous sexual partnerships based on cross-sectional samples and extended to apply to linked sexual partnerships with incomplete information on timing and direction of infection events and exposure information, and also generalized to account for missing cause of infection and account for measurement error in exposure data collected in studies of HIV transmission. The use of current status methods for multi- stage disease processes will be extended to address regression relationships between exposure factors and the onset of uterine fibroids based on follow-up information on an existing cohort of Italian women after their environmental exposure to dioxin. These methods can be generalized to studies of HIV infection where various stages of disease progression can be defined and exploited. A common theme underlying all these statistical tools is the joint and causal modeling of the effects of explanatory factors on time-to-event outcomes when there is incomplete information on both outcomes and cofactors, with data that is observational in nature and often high dimensional. The research will be carried out in collaboration with colleagues at the University of California, San Francisco, and other HIV researchers and epidemiologists who will allow access to data from their projects for analysis. The research has considerable relevance to public health in that it will allow the application of modern statistical ideas both to the analysis and interpretation of data (i) from randomized HIV intervention trials with complex intervention assignments and (ii) from observational studies, with incomplete data, of HIV transmission between sexual partners, and the effects of cofactors on disease initiation using disease diagnosis information.